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JournalISSN: 1552-6283

International Journal on Semantic Web and Information Systems 

IGI Global
About: International Journal on Semantic Web and Information Systems is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Semantic Web & Ontology (information science). It has an ISSN identifier of 1552-6283. Over the lifetime, 371 publications have been published receiving 14037 citations. The journal is also known as: International journal on Semantic Web & information systems & IJSWIS.


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Journal ArticleDOI
TL;DR: The authors describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked data community as it moves forward.
Abstract: The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

5,113 citations

Journal ArticleDOI
TL;DR: The Berlin SPARQL Benchmark (BSBM) as mentioned in this paper is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products.
Abstract: The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open Web settings. As SPARQL is taken up by the community, there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores as well as systems that rewrite SPARQL queries to SQL queries against non-RDF relational databases. This article introduces the Berlin SPARQL Benchmark (BSBM) for comparing the performance of native RDF stores with the performance of SPARQL-to-SQL rewriters across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix emulates the search and navigation pattern of a consumer looking for a product. The article discusses the design of the BSBM benchmark and presents the results of a benchmark experiment comparing the performance of four popular RDF stores (Sesame, Virtuoso, Jena TDB, and Jena SDB) with the performance of two SPARQL-to-SQL rewriters (D2R Server and Virtuoso RDF Views) as well as the performance of two relational database management systems (MySQL and Virtuoso RDBMS).

634 citations

Journal ArticleDOI
TL;DR: This article is an attempt to clarify the distinct roles for ontologies and folksonomies, and preview some new work that applies the two ideas together—an ontology of folk-sonomy.
Abstract: Ontologies are enabling technology for the Semantic Web. They are a means for people to state what they mean by the terms used in data that they might generate, share, or consume. Folksonomies are an emergent phenomenon of the social Web. They arise from data about how people associate terms with content that they generate, share, or consume. Recently the two ideas have been put into opposition, as if they were right and left poles of a political spectrum. This is a false dichotomy; they are more like apples and oranges. In fact, as the Semantic Web matures and the social Web grows, there is increasing value in applying Semantic Web technologies to the data of the social Web. This article is an attempt to clarify the distinct roles for ontologies and folksonomies, and preview some new work that applies the two ideas together—an ontology of folk-sonomy.

564 citations

Journal ArticleDOI
TL;DR: The authors review some of the recent developments on applying the semantic technologies based on machine-interpretable representation formalism to the Internet of Things.
Abstract: The Internet of Things IoT has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the "Things" makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT.

510 citations

Journal ArticleDOI
TL;DR: This work proposes the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms and demonstrates promising performance improvements over classic information retrieval methods utilizing plain lexical matching.
Abstract: Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies.

239 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202317
202258
202121
202026
201920
201831